Step 2: for Mac, simply click on the .pkg file, and open it once downloaded. For Windows systems, we need to click on install R for the first time and then on Download R for Windows. Once downloaded, open it like any installation file.
Step 3: open the installation executable.
Warning
Whenever you need to download something from CRAN (either R itself or a package), make sure you have an internet connection.
First operation
To check the installation, after opening R, you should see the R GUI (Graphical User Interface) with a white screen similar to this (console).
First code: we will assign the value 1 to a variable called a (we will write the code in the console and press “enter”). Then we will do the sum a + b.
a <-1
First operation
To check the installation, after opening R, you should see the R GUI (Graphical User Interface) with a white screen similar to this (console).
First code: we will assign the value 1 to a variable called a (we will write the code in the console and press “enter”). Then we will do the sum a + b.
a <-1b <-2
First operation
To check the installation, after opening R, you should see the R GUI (Graphical User Interface) with a white screen similar to this (console).
First code: we will assign the value 1 to a variable called a (we will write the code in the console and press “enter”). Then we will do the sum a + b.
a <-1b <-2a + b
[1] 3
Note that…
In the console, a number [1] appears: it’s simply an element counter (like counting rows in Word)
Installing R Studio
RStudio will be the Word we will use to write (what is known as an IDE: Integrated Development Environment).
Step 2: select the executable that appears according to your operating system.
Step 3: after downloading the executable, open it like any other and let the installation finish.
RStudio Organization
When you open RStudio you will likely have three windows:
Console: is the name for the large window that takes up most of your screen. Try writing the same code as before (the sum of the variables) in it. The console is where we will execute commands and display results.
RStudio Organization
When you open RStudio you will likely have three windows:
Environment: the small screen (you can adjust the margins with the mouse to your liking) that we have in the top right corner. It will show us the variables we have defined.
RStudio Organization
When you open RStudio you will likely have three windows:
Multi-purpose panel: the window at the bottom right will be used to look for function help, as well as to visualize plots.
What is R? Why R?
What is R? Why R?
R is the evolution of the work of Bell Laboratories with the S language, which was brought into the open-source world by Ross Ihaka and Robert Gentleman in the 1990s. The version R 1.0.0 was released on February 29, 2000.
What is R? Why R?
R is the statistical language par excellence, created by and for statisticians, with 6 fundamental advantages over Excel, SAS, Stata, or SPSS:
Programming language: the obvious → replicable analysis
Free: the philosophy of the R community is to share code under copyleft → ethical use of spending and algorithms
Open-source software: not only is it free, but it also allows free access to others’ code, even to the source code itself → flexibility and transparency (Free and Open Source Software FOSS)
What is R? Why R?
R is the statistical language par excellence, created by and for statisticians, with 6 fundamental advantages over Excel, SAS, Stata, or SPSS:
Modular language: we have installed the minimum, but there are codes from other people that we can reuse (almost 20,000 packages) → time saving and immediate innovation
Automate → it will allow you to automate recurring tasks.
Replicability → you will be able to replicate your analysis in the same way every time.
Flexibility → you will be able to adapt the software to your needs.
Transparency → to be audited by the community.
Fundamental Idea: Packages
One of the key ideas of R is the use of packages: codes that other people have implemented to solve a problem
Installation: we download the codes from the web (we need internet) → buy a book, only once (per computer)
install.packages("ggplot2")
Loading: with the package downloaded, we indicate which packages we want to use each time we open RStudio → take the book off the shelf
library(ggplot2)
Fundamental Idea: Packages
Once installed, there are two ways to use a package (take it off the shelf)
Whole package: with library(), using the package name without quotes, we load the whole book into the session
library(ggplot2)
Specific functions using `package::function+ we indicate that we only want a specific page of that book
ggplot2::geom_point()
You will be wrong
During your learning, it will be very common for things not to work out on the first try → you will be wrong. It will not only be important to accept it but also to read the error messages to learn from them.
Error messages: preceded by “Error in…” and will be those failures that prevent execution
"a"+1
Error in "a" + 1: non-numeric argument to binary operator
Warning messages: preceded by «Warning in…» they are the (possible) more delicate errors as they are inconsistencies that do not prevent execution
# Ejecuta la orden pero el resultado es NaN, **Not A Number**, un valor que no existesqrt(-1)
Warning in sqrt(-1): NaNs produced
[1] NaN
Scripts (.R files)
A script will be the document in which we program, our .doc file (here with a .R extension) where we will write the commands. To open our first script, click on the menu in File < New File < R Script.
Be careful
It’s important not to overuse the console: everything you don’t write in a script, when you close, will be lost.
Be careful
R is case-sensitive: it is sensitive to uppercase and lowercase, so x and X represent different variables.
Running the first script
Now we have a fourth window: the window where we will write our codes. How do we run it?
Write the code to be executed.
Save the .R file by clicking on Save current document.
The code does not execute unless we indicate it. We have three options to run a script:
Copy and paste into the console.
Select lines and press Ctrl+Enter
Enable Source on Save next to save: not only saves but also executes the entire code.
Organizing: projects
Just as we usually work organized by folders on the computer, in RStudio we can do the same to work efficiently by creating projects.
A project will be a “folder” within RStudio, so our root directory will automatically be the project folder itself (allowing us to switch from one project to another using the top right menu).
We can create one in a new folder or in an existing folder.”
📝 Create a course folder on your computer and set up an RStudio Project inside it. This will serve as your working directory for the entire course. After creating the project, you will see an .Rproj file. Within this folder, create two subfolders: data (for datasets) and scripts (for the .R files from each session).
📝 Inside the project create a script Exercises-class1.R (inside the scripts folder). Once created, define in it a variable named a and whose value is -1. Execute the code in the (three) ways explained before.
Code
a <--1
📝 Add below another line to define a variable b with the value 5. Then save the multiplication of both variables. Execute the code as you want.
Code
b <-5a * b # without saving itmult <- a * b # save it
📝 Modify the code below to define two variables c and d, with values 3 and -1. Then divide the variables and save the result.
c <-# you should assign 3d <-# you should assign -1
Code
c <-3d <--1c / ddiv <- c / d
📝 Assign to x a positive value and then compute its square root; assign to y a negative number and compute its absolute value using abs().
Code
x <-5sqrt(x)y <--2abs(y)
Note that…
Commands like sqrt(), abs() or max() are what we call functions: lines of code that we have “encapsulated” under a name, and given some input arguments, execute the commands (a sort of shortcut). In the functions the arguments will ALWAYS be enclosed in parentheses
📝 Using the variable x already defined, complete/modify the code below to store in a new variable z the result stored in x minus 5.
z <- ? - ? # complete the codez
Code
z <- x -5z
📝 Define an x variable and assign it the value -1. Define another y and assign it the value 0. Then perform the operations a) x by y; b) square root of x. What do you get?
Code
x <--1y <-0x / ysqrt(x)
📝 Write the code below in your script. Why do you think it doesn’t work?
x <--1y <-0X + y
Error: object 'X' not found
From CELL to TABLE
What data type can we have in each cell of a table?
Cell: an individual piece of data of a specific type.
Variable: concatenation of values of the same type (vectors in R).
Matrix: concatenation of variables of the same type and length.
Table: concatenation of variables of different types but the same length
List: concatenation of variables of different types and different lengths
But first…best practices
Before we continue, it’s important to know something as soon as possible: starting with programming can be frustrating
Just like when learning a new language, the first obstacle is not so much what to say but how to say it correctly. The same goes for R, so let’s standardize our programming style as much as possible to avoid future errors.
Tip 1: assignment, evaluation, and comparison are not the same. If you’ve noticed in R, we use <- to assign values to variables. We use = to evaluate function arguments and == to check if two elements are equal.
x <-1# asignx =1# evaluationx ==1# comparison
But first…best practices
Tip 2: program like you write. Just like when writing in Spanish, get used to incorporating spaces and line breaks to avoid making your code hard to read (it’s a good practice, not a requirement, because R does not process spaces).
x <-1# optimalx<-1# mehx<-1# worst (make up your mind)
Tip 3: don’t be chaotic, standardize names. Always get used to naming variables consistently. The only requirement is that they must always start with a letter (and without accents). The most recommended form is snake_case.
Tip 4: make reading and writing easier, set limits. In Tools < Global Options, you can customize some options in RStudio. In Code < Display, you can set Show margin to display an “imaginary” margin (not interacting with the code) to “force” you to make line breaks.
But first…best practices
Tip 5: the tab key is your best friend. In RStudio, there’s a wonderful tool: if you type part of a variable or function name and press tab, RStudio will autocomplete it for you.
But first…best practices
Tip 6: no single parentheses. Whenever you open a parenthesis, you must close it. To make this task easier, go to Tools < Global Options < Code < Display and enable the Rainbow parentheses option.
But first…best practices
Tip 7: pay attention to the left side. You will not only see the line of code you are on but also, in case of a syntax error, RStudio will notify you.
Tip 8: try to always work by projects (for this class, create a script class1.R in the project we created before)
Are there variables beyond numbers in data science? For example, think about the data you might store about a person:
Age or weight will be a number.
age <-33
Their name will be a string of text (known as string or char).
name <-"javi"
The answer to the question “Are you enrolled in the Faculty?” will be what we call a logical variable (TRUE if enrolled or FALSE otherwise).
enrolled <-TRUE
Their date of birth will be precisely that, a date.
Numerical variables
The simplest data (which we’ve already used) will be numeric variables. To find out the data class in R of a variable, we use the class() function.
a <-5
Numerical variables
The simplest data type (we have already used it) will be the numeric variables. To know the data class in R of a variable we have the function class().
a <-5class(a)
To know its typology (format) variable we have typeof().
typeof(1) # 1 value but stored as a real number (double precision)
[1] "double"
typeof(as.integer(1)) # 1 value but stored as a floor number
[1] "integer"
Note that…
In R we have a collection of functions starting with as.x() that serve as conversion functions: a data that was of one type, we convert it to type x.
Numerical variables
In addition to the “common” numbers we will have the plus/minus infinity coded as Inf or -Inf.
1/0
[1] Inf
-1/0
[1] -Inf
And values that are not real numbersnot a number (indeterminacies, complexes numbers, etc) encoded as NaN.
0/0
[1] NaN
sqrt(-2)
[1] NaN
Numerical variables
With numeric variables we can perform the arithmetic operations of a calculator: adding (+)…
a + b
[1] 7
…square root (sqrt())…
sqrt(a)
[1] 2.236068
… power (^2, ^3)…
a^2
[1] 25
…absolute value (abs()), etc.
abs(a)
[1] 5
String variables
Let us imagine that, in addition to the age of a person we want to store his/her name: now the variable will be of type character.
name <-"Javi"class(name)
[1] "character"
The text strings are a type with which we obviously cannot perform arithmetic operations (other operations such as pasting or locating patterns can be performed).
name +1# error when we try to sum 1 to a text
Error in name + 1: non-numeric argument to binary operator
Reminder
Text variables (character or string) are ALWAYS in quotes: TRUE (logical, binary value) is not the same as "TRUE" (text).
First function: paste
As we have commented R we will call function a piece of encapsulated code under a name, and which depends on some input arguments. Our first function will be paste(): given two strings, it allows us to paste them together.
paste("Javi", "Álvarez")
[1] "Javi Álvarez"
Note that default pastes strings with a space, but we can add an optional argument to tell it the separator (in sep = ...).
paste("Javi", "Álvarez", sep ="*")
[1] "Javi*Álvarez"
Remember that functions are always as name_of_function(arguments), whereas we will use [i] to access to i-th element.
First function: paste
How do I know what arguments does a function need?
By typing ? paste in the console, you will get a help in the multipurpose panel, where you can see in its header what arguments the function already has default arguments assigned to it.
There is a similar function called paste0() that pastes by default with sep = “” (without anything).
paste0("Javi", "Álvarez")
[1] "JaviÁlvarez"
First function: paste
The arguments (and their detail) can also be consulted by tabulating (after a comma).
Functions: default arguments
It is very important to understand the concept of default argument of a function in R: it is a value that the function uses but sometimes we may not see because already has a value assigned.
# Samepaste("Javi", "Álvarez")
[1] "Javi Álvarez"
paste("Javi", "Álvarez", sep =" ")
[1] "Javi Álvarez"
Note
The = operator is reserved for assigning arguments within functions. For all other assignments, we will use <-.
First package: glue
A more intuitive way to work with text is to use the {glue} package: the first thing to do is to “buy the book” (if we have never done it before). After that load the package
install.packages("glue") # just the first timelibrary(glue)
With the glue() function of that package we can use variables inside strings. For example, “age is … years old”, where the age is stored in a variable.
age <-34glue("I am {age} old")
I am 34 old
Within the keys we can also execute operations
units <-"days"glue("I am {age * 365} {units} old")
I am 12410 days old
Logical variables
Another fundamental type will be the logical or binary variables (two values):
TRUE: true stored internally as a 1.
FALSE: false stored internally as a 0.
single <-FALSE# Single? --> NOclass(single)
[1] "logical"
Since they are stored internally as binary variables, we can perform arithmetic operations on them
2*TRUE
[1] 2
FALSE-1
[1] -1
Logical variables
As we will see shortly, logical variables can actually take a third value: NA or missing data, representing not available, and it will be very common to find it within a database.
missing <-NAmissing +1
[1] NA
Important
Logical variables NOT text variables: "TRUE" is a text, TRUE is a logical value.
TRUE+1
[1] 2
"TRUE"+1
Error in "TRUE" + 1: non-numeric argument to binary operator
Logical conditions
Logical values are usually the result of evaluate logical conditions. For example, imagine that we want to check whether a person is named Javi.
name <-"María"
With the logical operator== we ask if what we have stored on the left is same as what we have on the right: we ASK
name =="Javi"
[1] FALSE
With its opposite != we ask if different.
name !="Javi"
[1] TRUE
Note that…
It is not the same <- (assignment) as == (we are asking, it is a logical comparison).
Logical conditions
In addition to “equal to” versus “different” comparisons, also order comparisons such as less than<, greater than>, <= or >=. Is the person less than 32 years old?
age <-34age <32# less than 32 years old?
[1] FALSE
Age is greater than or equal to 38 years?
age >=38
[1] FALSE
Is the saved name equal to Javi?
name <-"Javi"name =="Javi"
[1] TRUE
Date variables
A very special data type: the date type data.
date_char <-"2021-04-21"
It looks like a simple text string but should represent an instant in time. What should happen if we add a 1 to a date?
date_char +1
Error in date_char + 1: non-numeric argument to binary operator
Dates cannot be string/text: we must convert the text string to date.
To work with dates we will use the {lubridate} package, which we must install before we can use it.
install.packages("lubridate")
Date variables
Once installed, of all the packages (books) that we have, we will indicate it to load this one concretely.
library(lubridate)
To convert to date type we will use the as_date() function of the {lubridate} package (default in yyyy-mm-dd format).
# it's not a date, it's a text!date_char +1
Error in date_char + 1: non-numeric argument to binary operator
class(date_char)
[1] "character"
date <-as_date("2023-03-28")date +1
[1] "2023-03-29"
class(date)
[1] "Date"
Date variables
In as_date() the default date format is yyyy-mm-dd so if the string is not entered correctly…
as_date("28-08-2024")
[1] NA
For any other format we must specify it in the optional argument format = ... such that %d represents days, %m months, %Y in 4-year format and %y in 2-year format.
as_date("28-03-2023", format ="%d-%m-%Y")
[1] "2023-03-28"
as_date("28-03-23", format ="%d-%m-%y")
[1] "2023-03-28"
as_date("03-28-2023", format ="%m-%d-%Y")
[1] "2023-03-28"
as_date("28/03/2023", format ="%d/%m/%Y")
[1] "2023-03-28"
Date variables
In this package we have very useful functions for date management:
With today() we can directly obtain the current date.
today()
[1] "2026-02-16"
With now() we can obtain current date and time
now()
[1] "2026-02-16 13:31:28 EST"
With year(), month() or day() we can extract year, month and day
date_today <-today()year(date_today)
[1] 2026
month(date_today)
[1] 2
Cheatsheets
More information
You have a pdf summary of the most important packages in the corresponding folder on campus
💻 It’s your turn
Try to perform the following exercises without looking at the solutions
📝 Define a variable that stores your age (called age) and another with your name (called name).
Code
age <-34name <-"Javi"
📝 Check with this variable age if it is NOT 60 years old or if it is called "Ornitorrinco" (you must obtain logical variables as a result).
Code
age !=60# different toname =="Ornitorrinco"# equal to
📝 Why does the lower code not produce an error?
age + name
Error in age + name: non-numeric argument to binary operator
📝 Define another variable called siblings that answers the question “do you have siblings?” and another variable that stores your date of birth (called birth_date).
Code
siblings <-TRUElibrary(lubridate) # if not beforebirth_date <-as_date("1989-09-10")
📝 Define another variable with your last name (called surname) and use glue() to have, in a single variable called full_name, your first and last name separated by a comma.
When working with data, we often have columns that represent variables: we will refer to these as vectors, which are a concatenation of cells (values) of the same type (similar to a column in a table).
The simplest way to create a vector is with the c() function (c stands for concatenate), and you just need to input the elements within parentheses, separated by commas.
ages <-c(32, 27, 60, 61)ages
[1] 32 27 60 61
Tip
An individual number x <- 1 (or x <- c(1)) is actually a vector of length one –> everything we know how to do with a number, we can do with a vector of numbers.
Vectors: concatenation
As you can see now in the environment, we have a collection of elements stored.
ages # ages = edades in spanish
[1] 32 27 60 61
The length of a vector can be calculated with length().
length(ages)
[1] 4
We can also concatenate vectors together (it repeats them one after another).
c(ages, ages, 8)
[1] 32 27 60 61 32 27 60 61 8
Numeric sequences
The most common type of vector is numeric, specifically, the well-known numeric sequences (e.g., the days of the month), used among other things, to index loops.
The seq(start, end) function allows us to create a [**numeric sequence]**{.hl-yellow} from a starting element to an ending one, advancing one by one.
A vector is a concatenation of elements of the same type, but they don’t necessarily have to be numbers. Let’s create a sample sentence.
sentence <-"My name is Javi"sentence
[1] "My name is Javi"
length(sentence)
[1] 1
In the previous case, it wasn’t a vector, it was a single text element. To create a vector, we need to use c() again and separate elements with commas.
sentence <-c("My", "name", "is", "Javi")sentence
[1] "My" "name" "is" "Javi"
length(sentence)
[1] 4
String vectors
What will happen if we concatenate elements of different types?
c(1, 2, "javi", "3", TRUE)
[1] "1" "2" "javi" "3" "TRUE"
Note that since all elements must be of the same type, what R does is convert everything to text, violating the data integrity.
c(3, 4, TRUE, FALSE)
[1] 3 4 1 0
It’s important to understand that logical values are actually internally stored as 0/1.
Operations with vectors
With numeric vectors, we can perform the same arithmetic operations as with numbers → a number is a vector (of length one).
What will happen if we add or subtract a value to a vector?
x <-c(1, 3, 5, 7)x +1
[1] 2 4 6 8
x *2
[1] 2 6 10 14
Warning
Unless otherwise specified, in R, vector operations are always element by element.
Adding vectors
Vectors can also interact with each other, so we can define, for example, vector sums (element by element).
x <-c(2, 4, 6)y <-c(1, 3, 5)x + y
[1] 3 7 11
Since the operation (e.g., a sum) is performed element by element, what will happen if we add two vectors of different lengths?
z <-c(1, 3, 5, 7)x + z
[1] 3 7 11 9
What it does is recycle elements: if we have a vector of 4 elements and we add another with 3 elements, it will recycle the elements from the shorter vector.
Comparing vectors
A very common operation is to ask questions of the data using logical conditions. For example, if we define a vector of temperatures…
Which days were below 22 degrees?
x <-c(15, 20, 31, 27, 15, 29)
x <22
[1] TRUE TRUE FALSE FALSE TRUE FALSE
This will return a logical vector, depending on whether each element meets the given condition (of the same length as the vector being queried).
If we had a missing value (due to a sensor error that day), the evaluated condition would also be NA.
y <-c(15, 20, NA, 31, 27, 7, 29, 10)y <22
[1] TRUE TRUE NA FALSE FALSE TRUE FALSE TRUE
Comparing vectors
Logical conditions can be combined in two ways:
Intersection: all concatenated conditions must be met (AND conjunction with &) to return TRUE.
x <30& x >15
[1] FALSE TRUE FALSE TRUE FALSE TRUE
Union: it is enough for at least one condition to be met (OR conjunction with |).
x <30| x >15
[1] TRUE TRUE TRUE TRUE TRUE TRUE
With any() and all(), we can check if all elements satisfy the condition.
any(x <30)
[1] TRUE
all(x <30)
[1] FALSE
Getting elements
Another common operation is accessing or getting elements. The simplest way is to use the [i] operator (access the i-th element).
ages <-c(20, 30, 33, NA, 61) ages[3] # get the age's third person
[1] 33
Since a number is just a vector of length one, this operation can also be applied using a vector of indices to select.
y <-c("hi", "how", "are", "you", "?")y[c(1:2, 4)] # first, second and fourth element
[1] "hi" "how" "you"
Tip
To access the last element without worrying about its position, you can pass the vector’s length as the index x[length(x)].
Removing elements
Sometimes, instead of selecting, we may want to remove elements. This is done with the same operation but using negative indexing: the opetator [-i] «un-select» the i-th element
y
[1] "hi" "how" "are" "you" "?"
y[-2] # everything except the second element
[1] "hi" "are" "you" "?"
In many cases, we want to select or remove elements based on logical conditions, depending on the values, so we will pass the condition itself as the index (remember, x < 2 returns a logical vector).
ages <-c(15, 21, 30, 17, 45)names <-c("javi", "maría", "sandra", "carla", "luis")names[ages <18] # names of people under 18
[1] "javi" "carla"
Stats operations
We can also make use of statistical operations, such as sum(), which, given a vector, returns the sum of all its elements.
x <-c(1, -2, 3, -1)sum(x)
[1] 1
What happens when a data point is missing?
x <-c(1, -2, 3, NA, -1)sum(x)
[1] NA
By default, if we have a missing data point, the operation will also result in a missing value. To ignore that missing data, we use the optional argument na.rm = TRUE.
sum(x, na.rm =TRUE)
[1] 1
Stats operations
As we’ve mentioned, logical values are internally stored as 0 and 1, so we can use them in arithmetic operations.
For example, if we want to find out the number of elements that meet a condition (e.g., less than 3), those that do will be assigned a 1 (TRUE), and those that don’t will get a 0 (FALSE). Therefore, summing the logical vector will give us the number of elements that meet the condition.
x <-c(2, 4, 6)sum(x <3)
[1] 1
Stats operations
Another common operation that can be useful is the cumulative sum with cumsum(), which, given a vector, returns a vector where each element is the sum of the first, the first plus the second, the first plus the second plus the third, and so on.
x <-c(1, 5, 2, -1, 8)cumsum(x)
[1] 1 6 8 7 15
What happens when a data point is missing?
x <-c(1, -2, 3, NA, -1)cumsum(x)
[1] 1 -1 2 NA NA
In the case of the cumulative sum, what happens is that from that point onward, all subsequent accumulated values will be missing.
Stats operations
Another common operation that can be useful is the difference (with delay) with diff() which, given a vector, returns a vector with the second minus the first, the third minus the second, the fourth minus the third…and so on.
x <-c(1, 8, 5, 3, 9, 0, -1, 5)diff(x)
[1] 7 -3 -2 6 -9 -1 6
Using the argument lag = we can indicate the delay of this difference (e.g. lag = 3 implies that the fourth minus the first, the fifth minus the second, etc.).
x <-c(1, 8, 5, 3, 9, 0, -1, 5)diff(x, lag =3)
[1] 2 1 -5 -4 -4
Stats operations
Other common operations are mean, median, percentiles, etc.
mean: centrality measure that consists of adding all the elements and dividing by the number of elements added. The best known but the least robust: given a set, if outliers (very large or very small values) are introduced, the mean is very easily perturbed.
📝 Find from the vector x of exercises above the elements greater (strictly) than 1 and less (strictly) than 7. Find a way to find out if all the elements are positive or not.
Code
x[x >1& x <7]all(x >0)
📝 Given the vector x <- c(1, -5, 8, NA, 10, -3, 9), why does its mean return not a number but what is shown in the code below?
x <-c(1, -5, 8, NA, 10, -3, 9)mean(x)
[1] NA
📝 Given the vector x <- c(1, -5, 8, NA, 10, -3, 9), extract the elements occupying the locations 1, 2, 5, 6.
📝 Given the x vector of the previous exercise, which ones have a missing data? Hint: the is.something() functions check if the element is of type something (press tab).
Code
is.na(x)
📝 Define the vector x as the concatenation of the first 4 even numbers. Calculate the number of elements of x strictly less than 5.
Code
x[x <5] sum(x <5)
📝 Calculate the vector 1/x and obtain the ordered version (from smallest to largest) in the two possible ways
Code
z <-1/xsort(z)z[order(z)]
📝 Calculate min and max of previous x vector
Code
min(x)max(x)
📝 Find of the vector x the elements greater (strictly) than 1 and less (strictly) than 6. Find a way to find out if all the elements are negative or not.
Code
x[x >1& x <7]all(x >0)
First databases
When analyzing data we usually have several variables for each individual: we need a “table” to collect them. The most immediate option is matrices: concatenation of variables of same type and equal length.
Imagine we have heights and weights of 4 people. How to create a dataset with the two variables?
The most common option is to use cbind(): concatenate (bind) vectors in the form of columns (c)
h w
[1,] 150 63
[2,] 160 70
[3,] 170 85
[4,] 180 95
First databases
We can also build the matrix by rows with the rbind() function (concatenate - bind - by rows - r), although it is recommended to have each variable in column and individual in row as we will see later.
rbind(h, w) # Matrix by rows
[,1] [,2] [,3] [,4]
h 150 160 170 180
w 63 70 85 95
We can “view” the matrix with View(matrix).
We can check dimensions with dim(), nrow() and ncol(): matrices are a type of tabular data (organized in rows and columns).
dim(data_mat)
[1] 4 2
nrow(data_mat)
[1] 4
ncol(data_mat)
[1] 2
First databases
We can also “flip” (transposed matrix) with t().
t(data_mat)
[,1] [,2] [,3] [,4]
h 150 160 170 180
w 63 70 85 95
Since we now have two dimensions in our data, to access elements with [] we must provide two comma-separated indexes: row and column indexes
data_mat[2, 1] # second row, first column
h
160
data_mat[1, 2] # first row, second column
w
63
First databases
In some cases we will want to get the total data for an individual (a particular row but all columns) or the values of a whole variable for all individuals (a particular column but all rows). To do so, we leave one of the indexes unfilled.
data_mat[2, ] # second individual
h w
160 70
data_mat[, 1] # first variable
[1] 150 160 170 180
Much of what we have learned with vectors we can do with matrices, so we can for example access multiple rows and/or columns using the sequences of integers 1:n
data_mat[c(1, 3), 1] # first variable for first and third individual
[1] 150 170
First databases
We can also define a matrix from a numeric vector, rearranging the values in the form of a matrix (knowing that the elements are placed by columns).
z <-matrix(1:9, ncol =3) z
[,1] [,2] [,3]
[1,] 1 4 7
[2,] 2 5 8
[3,] 3 6 9
We can even define an array of constant values, e.g. of zeros (to be filled later)
matrix(0, nrow =2, ncol =3)
[,1] [,2] [,3]
[1,] 0 0 0
[2,] 0 0 0
Matrix operations
With matrices it is the same as with vectors: when we apply an arithmetic operation we do it element by element
📝 Define the matrix x <- matrix(1:12, nrow = 4). Then get the data of the first individual, the data of the third variable, and the element (4, 1).
Code
x <-matrix(1:12, nrow =4)x[1, ] # first rowx[, 3] # third columnx[4, 1] # (4, 1) element
📝 Define a matrix of 2 variables and 3 individuals such that each variable captures the height and age of 3 persons, so that the age of the second person is unknown (absent). Then calculate the mean of each variable (we should get a number!).
Code
data <-cbind("age"=c(20, NA, 25), "h"=c(160, 165, 170))apply(data, MARGIN =2, FUN ="mean", na.rm =TRUE) # mean by columns
📝 Why does the lower code return an error? What is wrong?
mat <-cbind("age"=c(15, 20, 25), "names"=c("javi", "sandra", "carlos"))mat
Error in mat + 1: non-numeric argument to binary operator
Second attempt: data.frame
Arrays have the same problem as vectors: if we put together data of different types, it data integrity is compromised as it converts them (see the code below: the ages and the TRUE/FALSE are converted to text).
ages single names
[1,] "14" "TRUE" "javi"
[2,] "24" NA "laura"
[3,] NA "FALSE" "lucía"
In fact, since they are not numbers, we can no longer perform arithmetic operations.
mat +1
Error in mat + 1: non-numeric argument to binary operator
Second attempt: data.frame
In order to work with variables of different type we have in R what is known as data.frame: concatenation of variables of equal length but which can be of different type.
table <-data.frame(ages, single, names)class(table)
[1] "data.frame"
table
ages single names
1 14 TRUE javi
2 24 NA laura
3 NA FALSE lucía
Second attempt: data.frame
Since a data.frame is already an attempt at a database the variables are not mere mathematical vectors: they have a meaning and we can (we must) give them names that describe their meaning.
library(lubridate)table <-data.frame("ages"= ages, "single"= single, "names"= names,"birth_date"=as_date(c("1989-09-10", "1992-04-01", "1980-11-27")))table
ages single names birth_date
1 14 TRUE javi 1989-09-10
2 24 NA laura 1992-04-01
3 NA FALSE lucía 1980-11-27
Second attempt: data.frame
We have our first data set! (strictly speaking we can’t talk about a database but for the moment it looks like one). You can visualize it by typing its name in console or with View(table).
Get variables
If we want to access its elements, being again tabulated data, we can access as in the matrices (not recommended): again we have two indexes (rows and columns, leaving free the one we don’t use)
table[2, ] # second row (all variables)
ages single names birth_date
2 24 NA laura 1992-04-01
table[, 3] # third column (all individuals)
[1] "javi" "laura" "lucía"
table[2, 1] # first variable of the second individual
[1] 24
But it also has the advantages of a database : we can access the variables by name (recommended since the variables can change position and now they have a meaning), putting the name of the table followed by the symbol $ (with the tab, a menu of columns to choose from will appear).
Ask functions
names(): shows us the variable names
names(table)
[1] "ages" "single" "names" "birth_date"
dim(): shows dimensions (also nrow() and ncol())
dim(table)
[1] 3 4
Variables can be accessed by name
table[c(1, 3), "names"]
[1] "javi" "lucía"
table$names[c(1, 3)]
[1] "javi" "lucía"
Add a variable
If we have one already created and we want to add a column it is as simple as using the data.frame() function we have already seen to concatenate the column. Let’s add for example a new variable, the number of siblings of each individual.
# add a new columnsiblings <-c(0, 2, 3)table <-data.frame(table, "n_sib"= siblings)table
ages single names birth_date n_sib
1 14 TRUE javi 1989-09-10 0
2 24 NA laura 1992-04-01 2
3 NA FALSE lucía 1980-11-27 3
Last attempt: tibble
Tables in data.frame format have some limitations. The main one is that does not allow recursion: imagine that we define a database with heights and weights, and we want a third variable with the BMI.
Each cell can be of a different type: numbers, text, dates, logical values, etc. A vector is a concatenation of cells (the future columns of our tables) –> In R by default operations are done element to element.
A matrix allows us to concatenate variables of the SAME type and SAME length –> tabular data.
A data.frame allows us to concatenate variables of DIFFERENT type and SAME length –> we will use tibble as an enhanced database option.
💻 It’s your turn
Try to perform the following exercises without looking at the solutions
📝 Load from the {datasets} package the airquality dataset (New York air quality variables from May through September 1973). Is the airquality dataset of type tibble? If not, convert it to tibble (look in the package documentation at https://tibble.tidyverse.org/index.html).
📝 Once converted to tibble get the name of the variables and the dimensions of the data set. How many variables are there? How many days have been measured?
The National Health and Nutrition Examination Survey (NHANES) is a large, nationally representative program conducted in the United States to assess the health and nutritional status of adults and children. NHANES combines interviews, physical examinations, and laboratory measurements. NHANES is widely used in epidemiology, public health research, and policy analysis.
# install.packages("NHANES")library(NHANES)NHANES
# A tibble: 6 × 15
ID SurveyYr Gender Age AgeDecade AgeMonths Race1 Race3 Education
<int> <fct> <fct> <int> <fct> <int> <fct> <fct> <fct>
1 51624 2009_10 male 34 " 30-39" 409 White <NA> High School
2 51624 2009_10 male 34 " 30-39" 409 White <NA> High School
3 51624 2009_10 male 34 " 30-39" 409 White <NA> High School
4 51625 2009_10 male 4 " 0-9" 49 Other <NA> <NA>
5 51630 2009_10 female 49 " 40-49" 596 White <NA> Some College
6 51638 2009_10 male 9 " 0-9" 115 White <NA> <NA>
# ℹ 6 more variables: MaritalStatus <fct>, HHIncome <fct>, HHIncomeMid <int>,
# Poverty <dbl>, HomeRooms <int>, HomeOwn <fct>
Try to answer the questions posed in the workbook intro-R
🐣 Case study II
In the {datasets} package (already installed by default) we have several datasets and one of them is airquality. Below I have extracted 3 variables from that dataset (note that it is done with data$variable, that dollar will be important in the future).The data captures daily measurements (n = 153 observations) of air quality in New York, from May to September 1973. Six 6 variables were measured: ozone levels, solar radiation, wind, temperature, month and day.
